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Output 2 - Smart IoT planters to collect data in learning spaces

Extended IO2 report available here.


The IoT system implemented in this IO has been designed to explore variables affecting plants and the environment in which they are located. It intends to support teachers in the creation of learning activities (i.e., learning designs) using IoT technology and indoor plants to train the digital green competences mentioned above. The interaction between stakeholders and the IoT system aims to inspire authentic learning experiences (i.e., feedback and feedforward) to promote awareness on the benefits of using plants in learning spaces.  The overall setup if the IoT system comprises the following tools:


The Spike is the core of the system. It is responsible for collecting data (sensors), storing it (internal and remote), and offering real-time feedback on it (actuators). All system components are orchestrated by an ESP32 micro-controller with Wi-Fi and Bluetooth connectivity. The case of the Spike was designed with a 3D printer. The upper part is shaped like a house and the microelectronics are isolated inside. The lower part is shaped like a stake to be able to insert it into a planter and obtain measurements about the soil of the plant. As illustrated in Fig. 3b, the roof is divided into two sides: the first side has a solar panel installed to recharge the batteries (inside the casing) that operate the entire system. Alternatively, the Spike can be powered using a micro-usb cable (5 V) if there is not enough charge. The other side of the roof is equipped with sensors and actuators. Here we describe the variables measured by these sensors and the arguments considered to install them in the Spike:


  • Carbon dioxide. As students and teachers exhale carbon dioxide (CO2) when they breathe, the concentration of this gas is an objective measure to determine if the air in the classroom is clean. High CO2 levels increase the chances to breathe air that was previously exhaled by another person. Therefore, the system makes it possible to observe how plants moderate CO2 levels and consequently apply the existing recommendations/regulations to ventilate classrooms.

  • Light. Plants need light to carry out photosynthesis. Natural photosynthesis is the process of capturing CO2 in the form of carbohydrate and releasing O2 using light as the energy source. 

  • Ambient temperature. Humans and plants carry out their vital functions in a more efficient way within certain ambient temperature ranges. The Sensirion-SCD30 sensor of the Spike monitors ambient temperature levels in Celsius degrees (°C) to optimise the well-being of plants and stakeholders in the classroom.

  • Ambient humidity. Humidity is a key variable to consider for obtaining a healthy environment for humans and plants. The Sensirion-SCD30 sensor of the Spike monitors relative humidity levels in percentage (%) to optimise the well-being of plants and stakeholders.

  • Soil moisture. This variable must be optimised considering the composition, the texture of the soil (clay, sandy) and the type of plant. Soil texture determines water filtration. The seed-studio capacitive soil moisture sensor of the Spike monitors soil humidity to automatise plant watering and to optimise plant growth. The capacitive soil moisture sensor, compared with resistive sensors, does not require direct exposure of the metal electrodes which can significantly reduce the erosion of the electrodes.

  • Soil temperature. Based on the scientific literature, the researchers of this work considered it interesting to empirically explore how soil temperature is correlated with the rest of the variables measured by the Spike (C02, humidity, etc.) in a learning context. 


The system allows to configure how often the data is read from the sensors. The Spike stores the data collected from sensors on a memory SD-card in spreadsheet format (i.e., Excel and CSV file). The SD-card can be ejected from the Spike to insert in a computer to analyse the data. When working in online mode, the Spike also uploads the data to the cloud. The system is equipped with two displays (actuators) to present the data. The 1.3-inch OLED is used to display alphanumeric data with the measurements collected by the sensors in real-time. In addition, the 12 multicoloured LED ring is using a traffic light metaphor to indicate the gradient for the monitored variable. The system represents a moment in which the CO2 variable is monitored (see asterisk on the OLED display). Three blue leds indicate optimum levels in the variable. Table 1 shows the correspondence between the configured thresholds and the feedback colour on the ring for the variables measured.


The system has evolved throughout the project in three different versions:


Spike version 1

This IoT system contains all the elements described above and was designed to be embedded in the soil of the planter itself.


Spike version 2

This IoT system improves the previous version by incorporating actuators (OLED RGB progressive bar) adapted to improve feedback usability in colorblind people. In addition, two temperature probes have been incorporated to be able to compare/contrast the measurements between two different plants. The system includes a chatbot to interact with the plant via text messages via Telegram. Unlike the previous version, this system was designed to be physically installed next to the pot as the dimensions of the spike could damage small potted plants.


Spike version 3

This IoT system includes functionalities that allow exploring learning activities related to the Internet of Things and artificial intelligence. These functionalities are: 1) a voice assistant that allows you to interact with the system through voice dialogues; 2) a mobile avatar (Plantagotchi) that allows the gardener to be given a virtual entity that allows illustrating how the plant reacts to different environmental conditions that may occur in the classroom; 3) an electrical differential meter that allows exploring how the plant reacts to different stimuli (for example: presence of people, music, noise, irrigation of the plant itself, or extreme variations of light or temperature).


The success of this IO is contrasted with the following tangible results:

- 4 final degree projects, 2 final master's projects, and 1 doctoral thesis (in progress) presented throughout the project.

- 4 Articles in international impact journals

- 3 Articles in international conferences.

- More than 20 learning activities features piloting the IoT system

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